Journal Articles

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https://hdl.handle.net/20.500.14170/296

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Browsing Journal Articles by Author "Ahmad Zia Ul-Saufie"
  • Publication
    Enhancing ecosystem biodiversity through air pollution concentrations prediction using support vector regression approaches
    (Universitatea Gheorghe Asachi din Iasi, 2023)
    Syaidatul Umairah Solehah
    ;
    Aida Wati Zainan Abidin
    ;
    Saiful Nizam Warris
    ;
    Wan Nur Shaziayani
    ;
    Balkish Mohd Osman
    ;
    Nurain Ibrahim
    ;
    Norazian Mohamed Noor
    ;
    Ahmad Zia Ul-Saufie
    Air is the most crucial element for the survival of life on Earth. The air we breathe has a profound effect on our ecosystem biodiversity. Consequently, it is always prudent to monitor the air quality in our environment. There are few ways can be done in predicting the air pollution index (API) like data mining. Therefore, this study aimed to evaluate three types of support vector regression (linear, SVR, libSVR) in predicting the air pollutant concentration and identify the best model. This study also would like to calculate the API by using the proposed model. The secondary daily data is used in this study from year 2002 to 2020 from the Department of Environment (DoE) Malaysia which located at Petaling Jaya monitoring station. There are six major pollutants that have been focusing in this work like PM10, PM2.5, SO2, NO2, CO, and O3. The root means square error (RMSE), mean absolute error (MAE) and relative error (RE) were used to evaluate the performance of the regression models. Experimental results showed that the best model is linear SVR with average of RMSE = 5.548, MAE = 3.490, and RE = 27.98% because had the lowest total rank value of RMSE, MAE, and RE for five air pollutants (PM10, PM2.5, SO2, CO, O3) in this study. Unlikely for NO2, the best model is support vector regression (SVR) with RMSE = 0.007, MAE = 0.006, and RE = 20.75% in predicting the air pollutant concentration. This work also illustrates that combining data mining with air pollutants prediction is an efficient and convenient way to solve some related environment problems. The best model has the potential to be applied as an early warning system to inform local authorities about the air quality and can reliably predict the daily air pollution events over three consecutive days. Besides, good air quality plays a significant role in supporting biodiversity and maintaning healthy ecosystems. © 2023 Universitatea "Alexandru Ioan Cuza" din Iasi. All rights reserved.
  • Publication
    Variability of PM10 level with gaseous pollutants and meteorological parameters during episodic haze event in Malaysia: domestic or solely transboundary factor?
    (Elsevier, 2023)
    Nur Alis Addiena A Rahim
    ;
    Norazian Mohamed Noor
    ;
    Izzati Amani Mohd Jafri
    ;
    Ahmad Zia Ul-Saufie
    ;
    Norazrin Ramli
    ;
    Ain Nihla Kamarudzaman
    ;
    Nor Amirah Abu Seman @ Haji Ahmad
    ;
    Mohd Remy Rozainy Mohd Arif Zainol
    ;
    Sandu Andrei Victor
    ;
    Gyorgy Deak
    Haze has become a seasonal phenomenon affecting Southeast Asia, including Malaysia, and has occurred almost every year within the last few decades. Air pollutants, specifically particulate matter, have drawn a lot of attention due to their adverse impact on human health. In this study, the spatial and temporal variability of the PM10 concentration at Kelang, Melaka, Pasir Gudang, and Petaling Jaya during historic haze events were analysed. An hourly dataset consisting of PM10, gaseous pollutants and weather parameters were obtained from Department of Environment Malaysia. The mean PM10 concentrations exceeded the stipulated Recommended Malaysia Ambient Air Quality Guideline for the yearly average of 150 μg/m3 except for Pasir Gudang in 1997 and 2005, and Petaling Jaya in 2013. The PM10 concentrations exhibit greater variability in the southwest monsoon and inter-monsoon periods at the studied year. The air masses are found to be originating from the region of Sumatra during the haze episodes. Strong to moderate correlation of PM10 concentrations was found between CO during the years that recorded episodic haze, meanwhile, the relationship of PM10 level with SO2 was found to be significant in 2013 with significant negatively correlated relative humidity. Weak correlation of PM10-NOx was measured in all study areas probably due to less contribution of domestic anthropogenic sources towards haze events in Malaysia.